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DeepInfer vs. vLLM: Benchmarking inference speed on B200 GPUs

By PulseAugur Editorial · [1 sources] ·

A new open-source inference framework called DeepInfer, developed by DeepSeek, has been benchmarked against vLLM on eight NVIDIA B200 GPUs. The integration, facilitated by GPUStack and a lightweight serving project called DeepInfer-server, aimed to evaluate real-world deployment performance. In tests using the DeepSeek-V4.1-Flash model, DeepInfer-server demonstrated higher reported output throughput and lower inter-token latency, while vLLM showed advantages in time to first token and P95 request latency. AI

IMPACT Provides insights into the performance differences between inference frameworks, aiding developers in choosing the optimal solution for their deployment needs.

RANK_REASON Benchmarking of open-source inference frameworks on new hardware. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DeepInfer vs. vLLM: Benchmarking inference speed on B200 GPUs

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Benchmarking of open-source inference frameworks on new hardware. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 Deutsch(DE) · GPUStack ·

    DeepInfer vs. vLLM on 8 B200: Which Is Faster?

    <p><strong>TL;DR:</strong> We integrated DeepInfer with GPUStack through a lightweight serving project, deployed DeepSeek-V4.1-Flash on a single server with eight NVIDIA B200 GPUs, and compared the resulting service with vLLM under a matched DP8+EP8 setup. In this run, DeepInfer-…

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